indexing module¶
Classes for indexing.
The main purpose of indexing classes is to provide pandas-like indexing to user-defined classes holding objects that have rows and/or columns. This is done by forwarding indexing commands to each structured object and constructing the new user-defined class using them. This way, one can manipulate complex classes with dozens of pandas objects using a single command.
build_param_indexer function¶
build_param_indexer(
param_names,
class_name='ParamIndexer',
module_name=None
)
A factory to create a class with parameter indexing.
Parameter indexer enables accessing a group of rows and columns by a parameter array (similar to loc). This way, one can query index/columns by another Series called a parameter mapper, which is just a pd.Series that maps columns (its index) to params (its values).
Parameter indexing is important, since querying by column/index labels alone is not always the best option. For example, pandas doesn't let you query by list at a specific index/column level.
Args
param_names:listofstr- Names of the parameters.
class_name:str- Name of the generated class.
module_name:str- Name of the module to which the class should be bound.
Usage
>>> import pandas as pd
>>> from vectorbt.base.indexing import build_param_indexer, indexing_on_mapper
>>> MyParamIndexer = build_param_indexer(['my_param'])
>>> class C(MyParamIndexer):
... def __init__(self, df, param_mapper):
... self.df = df
... self._my_param_mapper = param_mapper
... super().__init__([param_mapper])
...
... def indexing_func(self, pd_indexing_func):
... return self.__class__(
... pd_indexing_func(self.df),
... indexing_on_mapper(self._my_param_mapper, self.df, pd_indexing_func)
... )
>>> df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> param_mapper = pd.Series(['First', 'Second'], index=['a', 'b'])
>>> c = C(df, param_mapper)
>>> c.my_param_loc['First'].df
0 1
1 2
Name: a, dtype: int64
>>> c.my_param_loc['Second'].df
0 3
1 4
Name: b, dtype: int64
>>> c.my_param_loc[['First', 'First', 'Second', 'Second']].df
a b
0 1 1 3 3
1 2 2 4 4
indexing_on_mapper function¶
indexing_on_mapper(
mapper,
ref_obj,
pd_indexing_func
)
Broadcast mapper Series to ref_obj and perform pandas indexing using pd_indexing_func.
IndexingBase class¶
IndexingBase()
Class that supports indexing through IndexingBase.indexing_func().
Subclasses
- PandasIndexer
vectorbt.indicators.basic.ParamIndexervectorbt.indicators.basic.ParamIndexervectorbt.indicators.basic.ParamIndexervectorbt.indicators.basic.ParamIndexervectorbt.indicators.basic.ParamIndexervectorbt.indicators.basic.ParamIndexervectorbt.indicators.basic.ParamIndexervectorbt.indicators.basic.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.labels.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexervectorbt.signals.generators.ParamIndexer
indexing_func method¶
IndexingBase.indexing_func(
pd_indexing_func,
**kwargs
)
Apply pd_indexing_func on all pandas objects in question and return a new instance of the class.
Should be overridden.
IndexingError class¶
IndexingError(
*args,
**kwargs
)
Exception raised when an indexing error has occurred.
Superclasses
builtins.BaseExceptionbuiltins.Exception
Loc class¶
Loc(
indexing_func,
**kwargs
)
Forwards pd.Series.loc/pd.DataFrame.loc operation to each Series/DataFrame and returns a new class instance.
Superclasses
Inherited members
LocBase class¶
LocBase(
indexing_func,
**kwargs
)
Class that implements location-based indexing.
Subclasses
indexing_func property¶
Indexing function.
indexing_kwargs property¶
Keyword arguments passed to LocBase.indexing_func.
PandasIndexer class¶
PandasIndexer(
**kwargs
)
Implements indexing using iloc, loc, xs and __getitem__.
Usage
>>> import pandas as pd
>>> from vectorbt.base.indexing import PandasIndexer
>>> class C(PandasIndexer):
... def __init__(self, df1, df2):
... self.df1 = df1
... self.df2 = df2
... super().__init__()
...
... def indexing_func(self, pd_indexing_func):
... return self.__class__(
... pd_indexing_func(self.df1),
... pd_indexing_func(self.df2)
... )
>>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df2 = pd.DataFrame({'a': [5, 6], 'b': [7, 8]})
>>> c = C(df1, df2)
>>> c.iloc[:, 0]
<__main__.C object at 0x1a1cacbbe0>
>>> c.iloc[:, 0].df1
0 1
1 2
Name: a, dtype: int64
>>> c.iloc[:, 0].df2
0 5
1 6
Name: a, dtype: int64
Superclasses
Inherited members
Subclasses
iloc property¶
Forwards pd.Series.iloc/pd.DataFrame.iloc operation to each Series/DataFrame and returns a new class instance.
indexing_kwargs property¶
Indexing keyword arguments.
loc property¶
Forwards pd.Series.loc/pd.DataFrame.loc operation to each Series/DataFrame and returns a new class instance.
xs method¶
PandasIndexer.xs(
*args,
**kwargs
)
Forwards pd.Series.xs/pd.DataFrame.xs operation to each Series/DataFrame and returns a new class instance.
ParamLoc class¶
ParamLoc(
mapper,
indexing_func,
level_name=None,
**kwargs
)
Access a group of columns by parameter using pd.Series.loc.
Uses mapper to establish link between columns and parameter values.
Superclasses
Inherited members
get_indices method¶
ParamLoc.get_indices(
key
)
Get array of indices affected by this key.
level_name property¶
Level name.
mapper property¶
Mapper.
iLoc class¶
iLoc(
indexing_func,
**kwargs
)
Forwards pd.Series.iloc/pd.DataFrame.iloc operation to each Series/DataFrame and returns a new class instance.
Superclasses
Inherited members